Relevant bibliographies by topics / Grain protein deviation

Academic literature on the topic 'Grain protein deviation'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Grain protein deviation"

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Mosleth, Ellen F., Marie Lillehammer, Till K. Pellny, Abigail J. Wood, Andrew B. Riche, Abrar Hussain, Simon Griffiths, Malcolm J. Hawkesford, and Peter R. Shewry. "Genetic variation and heritability of grain protein deviation in European wheat genotypes." Field Crops Research 255 (September 2020): 107896. http://dx.doi.org/10.1016/j.fcr.2020.107896.

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Geyer, Manuel, Volker Mohler, and Lorenz Hartl. "Genetics of the Inverse Relationship between Grain Yield and Grain Protein Content in Common Wheat." Plants 11, no. 16 (August 18, 2022): 2146. http://dx.doi.org/10.3390/plants11162146.

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Grain protein content (GPC) is one of the most important criteria to determine the quality of common wheat (Triticum aestivum). One of the major obstacles for bread wheat production is the negative correlation between GPC and grain yield (GY). Previous studies demonstrated that the deviation from this inverse relationship is highly heritable. However, little is known about the genetics controlling these deviations in common wheat. To fill this gap, we performed quantitative trait locus (QTL) analysis for GY, GPC, and four derived GY-GPC indices using an eight-way multiparent advanced generation intercross population comprising 394 lines. Interval mapping was conducted using phenotypic data from up to nine environments and genotypic data from a 20k single-nucleotide polymorphism array. The four indices were highly heritable (0.76–0.88) and showed distinct correlations to GY and GPC. Interval mapping revealed that GY, GPC, and GY-GPC indices were controlled by 6, 12, and 12 unique QTL, of which each explained only a small amount of phenotypic variance (R2 ≤ 10%). Ten of the 12 index QTL were independent of loci affecting GY and GPC. QTL regions harboured several candidate genes, including Rht-1, WAPO-A1, TaTEF-7A, and NRT2.6-7A. The study confirmed the usefulness of indices to mitigate the inverse GY-GPC relationship in breeding, though the selection method should reflect their polygenic inheritance.
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Williams, P. C., and D. C. Sobering. "Comparison of Commercial near Infrared Transmittance and Reflectance Instruments for Analysis of Whole Grains and Seeds." Journal of Near Infrared Spectroscopy 1, no. 1 (January 1993): 25–32. http://dx.doi.org/10.1255/jnirs.3.

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Near infrared transmittance and reflectance instruments were compared for the determination of protein, oil, moisture and some other constituents and parameters in several grains and seeds of commerce. Both approaches were comparable in accuracy and reproducibility. The importance of optimisation of the wavelength range in whole grain analysis is demonstrated for measurements in both the NIR and visible/NlR wavelength ranges. The RPD statistic, which relates the standard error of prediction to the standard deviation of the original data, is illustrated as a method for the evaluation of calibrations. The concept of monitoring the accuracy of analysis using whole grain calibrations with ground grain calibrations is introduced.
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Mosleth, Ellen F., Yongfang Wan, Artem Lysenko, Gemma A. Chope, Simon P. Penson, Peter R. Shewry, and Malcolm J. Hawkesford. "A novel approach to identify genes that determine grain protein deviation in cereals." Plant Biotechnology Journal 13, no. 5 (November 14, 2014): 625–35. http://dx.doi.org/10.1111/pbi.12285.

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Thorwarth, Patrick, Hans P. Piepho, Yusheng Zhao, Erhard Ebmeyer, Johannes Schacht, Ralf Schachschneider, Ebrahim Kazman, Jochen C. Reif, Tobias Würschum, and Carl Friedrich Horst Longin. "Higher grain yield and higher grain protein deviation underline the potential of hybrid wheat for a sustainable agriculture." Plant Breeding 137, no. 3 (April 26, 2018): 326–37. http://dx.doi.org/10.1111/pbr.12588.

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Lutsey, Pamela L., David R. Jacobs, Sujata Kori, Elizabeth Mayer-Davis, Steven Shea, Lyn M. Steffen, Moyses Szklo, and Russell Tracy. "Whole grain intake and its cross-sectional association with obesity, insulin resistance, inflammation, diabetes and subclinical CVD: The MESA Study." British Journal of Nutrition 98, no. 2 (August 2007): 397–405. http://dx.doi.org/10.1017/s0007114507700715.

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We examined the relationship between whole grain intake and obesity, insulin resistance, inflammation, diabetes and subclinical CVD using baseline data from the Multi-Ethnic Study of Atherosclerosis. Whole grain intake was measured by a 127-item FFQ in 5496 men and women free of CHD and previously known diabetes. Mean whole grain intake was 0·5 (sd0·5) servings per d; biochemical measures reflect fasting levels. After adjustment for demographic and health behaviour variables, mean differences for the highest quintile of whole grain intake minus the lowest quintile of intake were 0·6 kg/m2for BMI, 0·36 mg/l for C-reactive protein, 0·82 μmol/l for homocysteine, 0·15 mU/l*mmol/l for homeostasis model assessment (HOMA), 0·48 mU/l for serum insulin, 2·0 mg/dl for glucose and 5·7 % for prevalence of newly diagnosed impaired fasting glucose (glucose ≥ 100 mg/dl or diabetes medication). These differences represent 11–13 % of a standard deviation of BMI, HOMA, glucose and impaired fasting glucose, but 23 %, 52 % and 80 % of a standard deviation of homocysteine, C-reactive protein and insulin, respectively. An inverse association between whole grains and urine albumin excretion was suggested but retained statistical significance after adjustment only in Chinese and Hispanic participants. No associations were observed between whole grain intake and two subclinical disease measures: carotid intima-media thickness and coronary artery calcification. Concordant with previous research, whole grain intake was inversely associated with obesity, insulin resistance, inflammation and elevated fasting glucose or newly diagnosed diabetes. Counter to hypothesis, however, whole grain intake was unrelated to subclinical CVD.
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Nigro, D., A. Gadaleta, G. Mangini, P. Colasuonno, I. Marcotuli, A. Giancaspro, S. L. Giove, R. Simeone, and A. Blanco. "Candidate genes and genome-wide association study of grain protein content and protein deviation in durum wheat." Planta 249, no. 4 (January 2, 2019): 1157–75. http://dx.doi.org/10.1007/s00425-018-03075-1.

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8

Latshaw, Susan P., Merle F. Vigil, and Scott D. Haley. "Genotypic Differences for Nitrogen Use Efficiency and Grain Protein Deviation in Hard Winter Wheat." Agronomy Journal 108, no. 6 (November 2016): 2201–13. http://dx.doi.org/10.2134/agronj2016.02.0070.

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Bogard, Matthieu, Vincent Allard, Maryse Brancourt-Hulmel, Emmanuel Heumez, Jean-Marie Machet, Marie-Hélène Jeuffroy, Philippe Gate, Pierre Martre, and Jacques Le Gouis. "Deviation from the grain protein concentration–grain yield negative relationship is highly correlated to post-anthesis N uptake in winter wheat." Journal of Experimental Botany 61, no. 15 (August 2, 2010): 4303–12. http://dx.doi.org/10.1093/jxb/erq238.

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Cheng, Weimin, Zhuopin Xu, Shuang Fan, Pengfei Zhang, Jiafa Xia, Hui Wang, Yafeng Ye, Binmei Liu, Qi Wang, and Yuejin Wu. "Effects of Variations in the Chemical Composition of Individual Rice Grains on the Eating Quality of Hybrid Indica Rice Based on Near-Infrared Spectroscopy." Foods 11, no. 17 (August 30, 2022): 2634. http://dx.doi.org/10.3390/foods11172634.

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The chemical composition of individual hybrid rice (F2) varieties varies owing to genetic differences between parental lines, and the effects of these differences on eating quality are unclear. In this study, based on a self-developed near-infrared spectroscopy platform, we explored these effects among a set of 143 hybrid indica rice varieties with different eating qualities. The single-grain amylose content (SGAC) and single-grain protein content (SGPC) models were established with coefficients of determination (R2) of 0.9064 and 0.8847, respectively, and the dispersion indicators (standard deviation, variance, extreme deviation, quartile deviation, and coefficient of variation) were proposed to analyze the variations in the SGAC and SGPC based on the predicted results. Our correlation analysis found that the higher the variation in the SGAC and SGPC, the lower the eating quality of the hybrid indica rice. Moreover, the addition of the dispersion indicators of the SGAC and SGPC improved the R2 of the eating quality model constructed using the composition-related physicochemical indicators (amylose content, protein content, alkali-spreading value, and gel consistency) from 0.657 to 0.850. Therefore, this new method proved to be useful for identifying high-eating-quality hybrid indica rice based on single near-infrared spectroscopy prior to processing and cooking.
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Dissertations / Theses on the topic "Grain protein deviation"

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Rahimi, Eichi Vahid. "Understanding the interactions between biomass, grain yield and grain protein content in low and high protein wheat cultivars." Thesis, 2020. http://hdl.handle.net/2440/129088.

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Grain protein content (GPC) is a key quality attribute and an important marketing trait in wheat. However, a negative relationship between grain yield and GPC has limited selection for increased GPC, since grain yield is the primary driver of breeding programs. GPC is strongly influenced by nitrogen (N) fertilizer application, but the N-use efficiency (NUE) of high and low GPC genotypes appears to be genetically determined. The aim of this PhD thesis was to investigate the grain yield-GPC relationship under controlled and field conditions, and to suggest selection targets and traits for improving NUE in wheat. Firstly, the N responsiveness of six wheat genotypes that varied in GPC were examined under controlled condition. This experiment was designed around non-destructive estimation of biomass using a high-throughput image-based phenotyping system. In parallel, field trials were conducted to allow the comparison of results obtained from the controlled condition study using the six selected genotypes. Estimating the rate of biomass accumulation in breeding plots in the field is difficult. Therefore, the growth rate of biomass related traits such as height and ground cover were assessed in these trials. To examine the grain yield- GPC relationship under multi-environmental conditions, the grain yield and GPC data of over 200 wheat genotypes obtained from the Australian National Variety Trials (NVT) across the Australian wheat-belt were analysed. Results of the controlled environment experiment showed that high GPC genotypes appeared to demand more N to grow their biomass. In both controlled and field environments, high GPC genotypes slowed down the rate of biomass growth under low N supply. Under low yielding conditions, high GPC genotypes seemed able to manage grain N reserves by compromising biomass production. These results indicated the importance of biomass growth analysis to show the differences in the N responsiveness of high and low GPC genotypes. Differences between high and low GPC genotypes in responding to low N could be due to their history of selection. N effect is strongly associated with the amount of available water in the soil. Controlled and multi-environmental studies showed that the slope of the relationship between grain yield and GPC is steeper in low compared to high yielding environments. Therefore, high GPC genotypes bred under stress conditions sacrifice yield in favour of GPC, possibly to enhance the survival chance by producing fewer grains with sufficient nutrient levels. Conversely, low GPC genotypes bred in high yielding environment are less conservative compared to high GPC genotypes in using N for yield production. The outcomes of this PhD project highlight the importance of considering environmental factors for improving NUE in breeding programs. It recommends that wheat breeders focus on selecting in low yielding environments for high yield and high GPC genotypes.
Thesis (Ph.D.) -- University of Adelaide, School of Agriculture, Food and Wine, 2020
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